Biblio

Filters: Author is Sumukhi Chandrashekar  [Clear All Filters]
2016-12-06
Sumukhi Chandrashekar.  2015.  Quantitative and qualitative evaluation of metrics on object graphs extracted by abstract interpretation. Computer Science. MS:103.
2016-12-05
Marwan Abi-Antoun, Sumukhi Chandrashekar, Radu Vanciu, Andrew Giang.  2014.  Are Object Graphs Extracted Using Abstract Interpretation Significantly Different from the Code? SCAM '14 Proceedings of the 2014 IEEE 14th International Working Conference on Source Code Analysis and Manipulation.

To evolve object-oriented code, one must understand both the code structure in terms of classes, and the runtime structure in terms of abstractions of objects that are being created and relations between those objects. To help with this understanding, static program analysis can extract heap abstractions such as object graphs. But the extracted graphs can become too large if they do not sufficiently abstract objects, or too imprecise if they abstract objects excessively to the point of being similar to a class diagram, where one box for a class represents all the instances of that class. One previously proposed solution uses both annotations and abstract interpretation to extract a global, hierarchical, abstract object graph that conveys both abstraction and design intent, but can still be related to the code structure. In this paper, we define metrics that relate nodes and edges in the object graph to elements in the code structure, to measure how they differ, and if the differences are indicative of language or design features such as encapsulation, polymorphism and inheritance. We compute the metrics across eight systems totaling over 100 KLOC, and show a statistically significant difference between the code and the object graph. In several cases, the magnitude of this difference is large.

Marwan Abi-Antoun, Sumukhi Chandrashekar, Radu Vanciu, Andrew Giang.  2014.  Are Object Graphs Extracted Using Abstract Interpretation Significantly Different from the Code? Extended Version SCAM '14 Proceedings of the 2014 IEEE 14th International Working Conference on Source Code Analysis and Manipulation.

To evolve object-oriented code, one must understand both the code structure in terms of classes, and the runtime structure in terms of abstractions of objects that are being created and relations between those objects. To help with this understanding, static program analysis can extract heap abstractions such as object graphs. But the extracted graphs can become too large if they do not sufficiently abstract objects, or too imprecise if they abstract objects excessively to the point of being similar to a class diagram that shows one box for a class to represent all the instances of that class. One previously proposed solution uses both annotations and abstract interpretation to extract a global, hierarchical, abstract object graph that conveys both abstraction and design intent, but can still be related to the code structure. In this paper, we define metrics that relate nodes and edges in the object graph to elements in the code structure to measure how they differ, and if the differences are indicative of language or design features such as encapsulation, polymorphism and inheritance. We compute the metrics across eight systems totaling over 100 KLOC, and show a statistically significant difference between the code and the object graph. In several cases, the magnitude of this difference is large.